What is blended learning?

At UW–Madison, blended courses are instructor-designed and supervised environments that use face-to-face and technology-mediated channels to enhance interactive, engaging learning experiences and to improve student learning outcomes. Blended courses should have the one or more of the following pedagogical characteristics:

  • a shift from teacher-centered instruction to student-centered learning;
  • significant interactions between student-instructor, student-student, and/or student-content; and/or
  • integrate formative and summative assessment for students and instructor.

On this campus, courses have three technology-mediation designations:

  • technology-enhanced: Less than 30% of the course includes technology-mediated learning activities.
  • blended: 30-99% of the course includes technology-mediated learning activities.
  • online: 100% of the course includes technology-mediated learning activities. – Blended Learning Fellowship Program

What is active learning?

Within a blended course at UW-Madison, active learning is the outcome of an experiential teaching approach that facilitates increased student engagement and higher-order cognition through instructor-guided, learner-centered, thought-provoking interactions in all components of the course (pre-class, in-class, and post-class). These interactions are meant to demonstrate discipline-specific methodological approaches to knowledge, align with course/program learning outcomes, provide evidence of learning to instructor and students, and reveal the learning processes students use. Examples of active learning include interactions such as: reflection, discussion, exploration, collaboration, experimentation, analysis, and construction of new knowledge. To learn more about active learning approaches, go to the Using Active Learning in the Classroom resource.”  – Blended Learning Fellowship on Active Learning

What is learning analytics?

“Learning analytics is the measurement, collection, analysis, and reporting of data about learners and their contexts for purposes of understanding and optimizing learning and the environments in which it occurs.” – Society for Learning Analytics Research

Learning Analytics Functional Taxonomy

Are you wondering how you can include learning analytics practices in your teaching? What data can you access that might help you answer questions you have about your course, your students and your teaching?

This taxonomy was initially explored and adapted by the Blended Learning Fellowship on Evidence-Based Teaching in the spring of 2018.  Six different learning analytics approaches were selected. We will continue to add examples to this taxonomy, including local use cases.

  • Access Learning Behavior
  • Evaluate Social Learning
  • Improve Learning Materials & Tools
  • Individualized Learning
  • Predict Student Performance
  • Visualize Learning Activities

View the Learning Analytics Functional Taxonomy